Agentic Plugin Marketplace is a collection of reusable plugins, agents, skills, commands, and rules for coding-agent tools including Claude Code, Codex CLI, Cursor, OpenCode, Antigravity CLI, and GitHub Copilot. It is for developers assembling agentic workflows across multiple harnesses from shared Markdown sources, and the catalogue entries are examples or subsets of those workflow components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wshobson/agents --skill spark-environment-setupgit clone --depth 1 https://github.com/wshobson/agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wshobson/agents/spark-environment-setup)<a href="https://agentmods.dev/skills/wshobson/agents/spark-environment-setup"><img src="https://agentmods.dev/badge/skills/wshobson/agents/spark-environment-setup/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wshobson/agents/spark-environment-setup"><img src="https://agentmods.dev/badge/skills/wshobson/agents/spark-environment-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 47 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00076 | $0.02086 |
| Opus 5.5 | $0.00030 | $0.00834 |
| Sonnet 5.5 | $0.00015 | $0.00417 |
| Haiku 4.5 | $0.00008 | $0.00209 |
Grade A, and why
spark-environment-setup scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spark Environment Setup
DGX Spark ships a GB10 Grace Blackwell chip: aarch64 CPU, SM121 GPU, 128GB unified memory, CUDA 13. This is a narrower and younger platform than a standard x86 CUDA 12 box, so package selection and ABI matching matter more than usual — the wheel ecosystem for aarch64 + CUDA 13 is still filling in.
When to Use This Skill
- Setting up a fresh Spark box for training or inference.
- Hitting an import error mentioning
libcudart, a missing symbol, or a wheel that "installed fine but won't load." - A framework install (PyTorch, Unsloth, TRL, vLLM, xformers) fails, hangs, or silently falls back to CPU.
- Deciding whether to use an NGC container or bare pip.
- Restoring a working setup after an OS reinstall or a base-image update, needing to re-verify from scratch.
Each of these accepts the same general fix: match the container/wheel combination to CUDA 13 and SM121, don't fight the ABI.
Container-First Rule
Quick decision, before the detail below:
- Standard training/inference work → NGC PyTorch container.
- Unsloth-centric fine-tuning → Unsloth container (it ships the pinned Triton/xformers/transformers combination already validated for that path).
- Neither fits (custom system package, local IDE interpreter) → bare pip, following the exact sequence further down.
Default to a container. Use nvcr.io/nvidia/pytorch:25.09-py3
as the base for general work — the newest tag confirmed working
on this hardware; pull a newer blessed tag if locally available
rather than hard-blocking on 25.11-py3. NGC's tag is dated, so
running it directly is fine:
docker run --runtime=nvidia --gpus all -it --rm \
nvcr.io/nvidia/pytorch:25.09-py3
unsloth/unsloth:dgxspark-latest is a moving tag by
contrast — resolve and pin its digest before running it for
anything reproducible; the bare tag is a discovery step only,
not the default invocation. Full pull-inspect-pin sequence and
flag rationale/volume mounts for finetuning/ run dirs:
references/container-workflow.md. Treat bare pip as the exception.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 201 lines · 76 tokens per session scan A 4fe4b4328a52
spark-environment-setup is a skill published in the GitHub repository wshobson/agents (41,158 stars, last pushed today), licensed MIT. It adds 76 tokens to every session and 2,086 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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